Molecular Dynamics Simulations and Computer-Aided Drug Discovery

  • Ryan C. Godwin
  • Ryan Melvin
  • Freddie R. SalsburyJr.
Protocol

Abstract

Molecular dynamics simulations of biomolecules, proteins especially, have emerged as an important tool in the study of the conformational change, flexibility, and dynamics. These simulations, especially when combined with virtual screening, have been a tool in drug discovery. Herein, we cover the basics of molecular dynamics simulation, in the hopes that a reader would be able to intelligently conduct a simulation of their favorite protein(s), analyze the results in order to make hypotheses about the links between protein dynamics and conformation. We also discuss the integration between molecular dynamics and virtual screening, so that a reader could use the results of simulations to perform virtual screening for lead identification. Finally, we review several case studies to show what sort of information can be gained by simulation of biomedically interesting proteins, and how that may impact drug discovery, as well as a discussion of some areas in which simulation may prove more useful in the near future.

Key words

Molecular dynamics Simulations Drug discovery Markov analysis Protein dynamics Acmed 

References

  1. 1.
    Mccammon JA, Gelin BR, Karplus M (1977) Dynamics of folded proteins. Nature 267:585–590PubMedCrossRefGoogle Scholar
  2. 2.
    Radkiewicz JL, Brooks CLI (2000) Protein dynamics in enzymatic catalysis: exploration of dihydrofolate reductase. J Am Chem Soc 122:225–231. doi:10.1021/ja9913838 CrossRefGoogle Scholar
  3. 3.
    Salsbury F (2001) Modeling of the metallo‐β‐lactamase from B. fragilis: structural and dynamic effects of inhibitor binding. Proteins 44:448–459PubMedCrossRefGoogle Scholar
  4. 4.
    Salsbury FR, Crowder MW, Kingsmore SF, Huntley JJ (2009) Molecular dynamic simulations of the metallo-beta-lactamase from Bacteroides fragilis in the presence and absence of a tight-binding inhibitor. J Mol Model 15:133–145. doi:10.1007/s00894-008-0410-0 PubMedCrossRefGoogle Scholar
  5. 5.
    Kumar S, Ma B, Tsai C-J et al (2000) Folding and binding cascades: dynamic landscapes and population shifts. Protein Sci 9:10–19PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Freire E (1999) The propagation of binding interactions to remote sites in proteins: analysis of the binding of the monoclonal antibody. Proc Natl Acad Sci 96:10118–10122PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Kern D, Zuiderweg ER (2003) The role of dynamics in allosteric regulation. Curr Opin Struct Biol 13:748–757. doi:10.1016/j.sbi.2003.10.008 PubMedCrossRefGoogle Scholar
  8. 8.
    Pan H, Lee JC, Hilser VJ (2000) Binding sites in Escherichia coli dihydrofolate reductase communicate by modulating the conformational ensemble. Proc Natl Acad Sci U S A 97:12020–12025. doi:10.1073/pnas.220240297 PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Gunasekaran K, Ma B, Nussinov R (2004) Is allostery an intrinsic property of all dynamic proteins? Proteins 57:433–443. doi:10.1002/prot.20232 PubMedCrossRefGoogle Scholar
  10. 10.
    Tsai C, Ma B, Nussinov R (1999) Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci U S A 96:9970–9972PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Vasilyeva A, Clodfelter JE, Rector B et al (2009) Small molecule induction of MSH2-dependent cell death suggests a vital role of mismatch repair proteins in cell death. DNA Repair (Amst) 8:103–113. doi:10.1016/j.dnarep.2008.09.008 CrossRefGoogle Scholar
  12. 12.
    Salsbury FR (2010) Molecular dynamics simulations of protein dynamics and their relevance to drug discovery. Curr Opin Pharmacol 10:738–744. doi:10.1016/j.coph.2010.09.016 PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Berman HM (2000) The protein data bank. Nucleic Acids Res 28:235–242. doi:10.1093/nar/28.1.235 PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Saxena A, Sangwan RS, Mishra S (2013) Fundamentals of homology modeling steps and comparison among important bioinformatics tools: an overview. Sci Int 1:237–252. doi:10.5567/sciintl.2013.237.252 CrossRefGoogle Scholar
  15. 15.
    Szalewicz K (2014) Determination of structure and properties of molecular crystals from first principles. Acc Chem Res 47:3266–3274. doi:10.1021/ar500275m PubMedCrossRefGoogle Scholar
  16. 16.
    MacKerell A, Bashford D (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem 5647:3586–3616. doi:10.1021/jp973084f CrossRefGoogle Scholar
  17. 17.
    Ponder JW, Case DA (2003) Force fields for protein simulations. Adv Protein Chem 66:27–85. doi:10.1016/S0065-3233(03)66002-X PubMedCrossRefGoogle Scholar
  18. 18.
    Oostenbrink C, Villa A, Mark AE, Van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25:1656–1676. doi:10.1002/jcc.20090 PubMedCrossRefGoogle Scholar
  19. 19.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N log(N) method for Ewald sums in large systems. J Chem Phys 12:10089–10092CrossRefGoogle Scholar
  20. 20.
    Roe DR, Okur A, Wickstrom L et al (2007) Secondary structure bias in generalized Born solvent models: comparison of conformational ensembles and free energy of solvent polarization from explicit and implicit solvation. J Phys Chem B 111:1846–1857. doi:10.1021/jp066831u PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    García AE, Sanbonmatsu KY (2002) Alpha-helical stabilization by side chain shielding of backbone hydrogen bonds. Proc Natl Acad Sci U S A 99:2782–2787. doi:10.1073/pnas.042496899 PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Feig M, MacKerell AD, Brooks CL (2003) Force field influence on the observation of π-helical protein structures in molecular dynamics simulations. J Phys Chem B 107:2831–2836. doi:10.1021/jp027293y CrossRefGoogle Scholar
  23. 23.
    Lee MS, Salsbury FR, Brooks CL (2002) Novel generalized born methods. J Chem Phys 116:10606. doi:10.1063/1.1480013 CrossRefGoogle Scholar
  24. 24.
    The 2013 Nobel Prize in ChemistryGoogle Scholar
  25. 25.
    Phillips JC, Braun R, Wang W et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802. doi:10.1002/jcc.20289 PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Brooks B, Brooks C (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614. doi:10.1002/jcc.21287.CHARMM PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688. doi:10.1002/jcc.20290 PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447. doi:10.1021/ct700301q PubMedCrossRefGoogle Scholar
  29. 29.
    Le L, Lee E, Schulten K, Truong TN (2009) Molecular modeling of swine influenza A/H1N1, Spanish H1N1, and avian H5N1 flu N1 neuraminidases bound to Tamiflu and Relenza. PLoS Curr 1:RRN1015. doi:10.1371/currents.RRN1015 PubMedGoogle Scholar
  30. 30.
    De Meyer FJ-M, Venturoli M, Smit B (2008) Molecular simulations of lipid-mediated protein-protein interactions. Biophys J 95:1851–1865. doi:10.1529/biophysj.107.124164 PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Harvey M, Giupponi G, Fabritiis G (2009) ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J Chem Theory Comput 5:1632PubMedCrossRefGoogle Scholar
  32. 32.
    Schlick T (2010) Molecular modeling and simulation: an interdisciplinary guide: an interdisciplinary guide. Springer Science & Business Media, New York, NYCrossRefGoogle Scholar
  33. 33.
    Frenkel D, Smit B (2001) Understanding molecular simulation: from algorithms to applications. Academic, San Diego, CAGoogle Scholar
  34. 34.
    Fenimore PW, Frauenfelder H, Mcmahon BH, Parak FG (2002) Slaving: solvent fluctuations dominate protein dynamics and functions. Proc Natl Acad Sci U S A 99:16047–16051PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Frauenfelder H, Fenimore PW, Young RD (2007) Protein dynamics and function: insights from the energy landscape and solvent slaving. IUBMB Life 59:506–512. doi:10.1080/15216540701194113 PubMedCrossRefGoogle Scholar
  36. 36.
    Tarek M, Tobias DJ (2000) The dynamics of protein hydration water: a quantitative comparison of molecular dynamics simulations and neutron-scattering experiments. Biophys J 79:3244–3257. doi:10.1016/S0006-3495(00)76557-X PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Berendsen HJC, Postma JPM, van Gunsteren WF, Hermans J (1981) Interaction models for water in relation to protein hydration. In: Pullman B (ed) Intermolecular forces. Springer, Berlin, pp 331–342CrossRefGoogle Scholar
  38. 38.
    Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926. doi:10.1063/1.445869 CrossRefGoogle Scholar
  39. 39.
    Zhou R (2003) Free energy landscape of protein folding in water: explicit vs. implicit solvent. Proteins 161:148–161CrossRefGoogle Scholar
  40. 40.
    Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A 105:9954–9960. doi:10.1021/jp003020w CrossRefGoogle Scholar
  41. 41.
    Ryckaert J, Ciccotti G, Berendsen H (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 341:327–341CrossRefGoogle Scholar
  42. 42.
    Knight JL, Brooks CL (2011) Surveying implicit solvent models for estimating small molecule absolute hydration free energies. J Comput Chem 32:2909–2923. doi:10.1002/jcc.21876 PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Pu M, Garrahan JP, Hirst JD (2011) Comparison of implicit solvent models and force fields in molecular dynamics simulations of the PB1 domain. Chem Phys Lett 515:283–289. doi:10.1016/j.cplett.2011.09.026 CrossRefGoogle Scholar
  44. 44.
    Zhou R, Berne B (2002) Can a continuum solvent model reproduce the free energy landscape of a β-hairpin folding in water? Proc Natl Acad Sci U S A 99:12777PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Lee MS, Feig M, Salsbury FR, Brooks CL III (2003) New analytic approximation to the standard molecular volume definition and its application to generalized born calculations. J Comput Chem 24:1348PubMedCrossRefGoogle Scholar
  46. 46.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33PubMedCrossRefGoogle Scholar
  47. 47.
    Heyer L, Kruglyak S, Yooseph S (1999) Exploring expression data: identification and analysis of coexpressed genes. Genome Res 9:1106–1115PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Karpen M, Tobias D, Brooks C III (1993) Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV. Biochemistry 32:412–420PubMedCrossRefGoogle Scholar
  49. 49.
    Brooks BR, Bruccoleri RE, Olafson BD et al (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217. doi:10.1002/jcc.540040211 CrossRefGoogle Scholar
  50. 50.
    Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process 37:54–115. doi:10.1016/S0734-189X(87)80014-2 CrossRefGoogle Scholar
  51. 51.
    Pao Y-H (1989) Adaptive pattern recognition and neural networks. Addison-Wesley Longman Publishing Co., Inc., Boston, MAGoogle Scholar
  52. 52.
    Senne M, Schütte C, Noé F (2012) EMMA: a software package for Markov model building and analysis. J Chem Theory Comput 8:2223–2228PubMedCrossRefGoogle Scholar
  53. 53.
    Beauchamp K, Bowman G (2011) MSMBuilder2: modeling conformational dynamics on the picosecond to millisecond scale. J Chem Theory Comput 7:3412–3419. doi:10.1021/ct200463m.MSMBuilder2 PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Cronkite-Ratcliff B, Pande V (2013) MSMExplorer: visualizing Markov state models for biomolecule folding simulations. Bioinformatics 29:950–952. doi:10.1093/bioinformatics/btt051 PubMedCrossRefGoogle Scholar
  55. 55.
    Pande V, Beauchamp K, Bowman G (2010) Everything you wanted to know about Markov State Models but were afraid to ask. Methods 52:99–105. doi:10.1016/j.ymeth.2010.06.002.Everything PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Kozakov D, Hall DR, Chuang G-Y et al (2011) Structural conservation of druggable hot spots in protein-protein interfaces. Proc Natl Acad Sci U S A 108:13528–13533. doi:10.1073/pnas.1101835108 PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Peng JW (2009) Communication breakdown: protein dynamics and drug design. Structure 17:319–320. doi:10.1016/j.str.2009.02.004 PubMedCrossRefGoogle Scholar
  58. 58.
    Mauldin RV, Carroll MJ, Lee AL (2009) Dynamic dysfunction in dihydrofolate reductase results from antifolate drug binding: modulation of dynamics within a structural state. Structure 17:386–394. doi:10.1016/j.str.2009.01.005 PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Negureanu L, Salsbury FR (2012) Insights into protein - DNA interactions, stability and allosteric communications: a computational study of mutSα-DNA recognition complexes. J Biomol Struct Dyn 29:757–776. doi:10.1080/07391102.2012.10507412 PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Godwin RC, Gmeiner WH, Salsbury FR (2015) Importance of long-time simulations for rare event sampling in zinc finger proteins. J Biomol Struct Dyn (In press). doi:10.1080/07391102.2015.1015168
  61. 61.
    Ichiye T, Karplus M (1991) Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins 11:205–217PubMedCrossRefGoogle Scholar
  62. 62.
    Amadei A, Linssen A, Berendsen HJC (1993) Essential dynamics of proteins. Proteins 17:412–425PubMedCrossRefGoogle Scholar
  63. 63.
    Schäfer H, Mark AE, Van Gunsteren WF (2000) Absolute entropies from molecular dynamics simulation trajectories. J Chem Phys 113:7809–7817. doi:10.1063/1.1309534 CrossRefGoogle Scholar
  64. 64.
    Andricioaei I, Karplus M (2001) On the calculation of entropy from covariance matrices of the atomic fluctuations. J Chem Phys 115:6289–6292. doi:10.1063/1.1401821 CrossRefGoogle Scholar
  65. 65.
    Huang Z, Wong C (2009) Docking flexible peptide to flexible protein by molecular dynamics using two implicit-solvent models: an evaluation in protein kinase and phosphatase systems. J Phys Chem B 113:14343–14354. doi:10.1021/jp907375b.Docking PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Knegtel RM, Kuntz ID, Oshiro CM (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266:424–440. doi:10.1006/jmbi.1996.0776 PubMedCrossRefGoogle Scholar
  67. 67.
    Claussen H, Buning C, Rarey M, Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308:377–395. doi:10.1006/jmbi.2001.4551 PubMedCrossRefGoogle Scholar
  68. 68.
    Lin J-H, Perryman AL, Schames JR, McCammon JA (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J Am Chem Soc 124:5632–5633. doi:10.1021/ja0260162 PubMedCrossRefGoogle Scholar
  69. 69.
    Frembgen-Kesner T, Elcock AH (2006) Computational sampling of a cryptic drug binding site in a protein receptor: explicit solvent molecular dynamics and inhibitor docking to p38 MAP kinase. J Mol Biol 359:202–214. doi:10.1016/j.jmb.2006.03.021 PubMedCrossRefGoogle Scholar
  70. 70.
    Tatsumi R, Fukunishi Y, Nakamura H (2004) A hybrid method of molecular dynamics and harmonic dynamics for docking of flexible ligand to flexible receptor. J Comput Chem 25:1995–2005. doi:10.1002/jcc.20133 PubMedCrossRefGoogle Scholar
  71. 71.
    Lin J, Perryman A (2003) The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 68:47–62PubMedCrossRefGoogle Scholar
  72. 72.
    Totrov M, Abagyan R (1997) Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins 220:215–220CrossRefGoogle Scholar
  73. 73.
    Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8:195–202. doi:10.1002/prot.340080302 PubMedCrossRefGoogle Scholar
  74. 74.
    Goodsell D (1996) Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 9:1–5PubMedCrossRefGoogle Scholar
  75. 75.
    Trott O, Olson AJ (2010) Software news and update AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. doi:10.1002/jcc PubMedPubMedCentralGoogle Scholar
  76. 76.
    Morris G, Goodsell D (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662CrossRefGoogle Scholar
  77. 77.
    Morris G, Huey R (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. doi:10.1002/jcc PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Wang R, Fang X, Lu Y, Wang S (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem 47:2977–2980. doi:10.1021/jm030580l PubMedCrossRefGoogle Scholar
  79. 79.
    Wang R, Fang X, Lu Y et al (2005) The PDBbind database: methodologies and updates. J Med Chem 48:4111–4119. doi:10.1021/jm048957q PubMedCrossRefGoogle Scholar
  80. 80.
    Abagyan R, Totrov M, Kuznetsov D (1994) ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15:488–506CrossRefGoogle Scholar
  81. 81.
    Blum C, Roli A, Sampels M (2008) Hybrid metaheuristics: an emerging approach to optimization. Springer, New York, NYCrossRefGoogle Scholar
  82. 82.
    Nocedal J, Wright SJ (1999) Numerical optimization. Springer, New York, NYCrossRefGoogle Scholar
  83. 83.
    Liu T, Lin Y, Wen X et al (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35:D198–D201. doi:10.1093/nar/gkl999 PubMedCrossRefGoogle Scholar
  84. 84.
    Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107. doi:10.1093/nar/gkr777 PubMedCrossRefGoogle Scholar
  85. 85.
    Knox C, Law V, Jewison T et al (2011) DrugBank 3.0: a comprehensive resource for “omics” research on drugs. Nucleic Acids Res 39:D1035–D1041. doi:10.1093/nar/gkq1126 PubMedCrossRefGoogle Scholar
  86. 86.
    Li Q, Cheng T, Wang Y, Bryant S (2010) PubChem as a public resource for drug discovery. Drug Discov Today 15:1052–1057. doi:10.1016/j.drudis.2010.10.003.PubChem PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Chen C (2011) TCM Database@ Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One 6:e15939. doi:10.1371/journal.pone.0015939 PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Irwin JJ, Shoichet BK (2005) ZINC - a free database of commercially available compounds for virtual screening. J Chem Inf Model 36:177–182. doi:10.1002/chin.200516215 CrossRefGoogle Scholar
  89. 89.
    Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768. doi:10.1021/ci3001277 PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Schüller A, Hähnke V, Schneider G (2007) SmiLib v2.0: a Java-based tool for rapid combinatorial library enumeration. QSAR Comb Sci 26:407–410. doi:10.1002/qsar.200630101 CrossRefGoogle Scholar
  91. 91.
    Salsbury FR, Clodfelter JE, Gentry MB et al (2006) The molecular mechanism of DNA damage recognition by MutS homologs and its consequences for cell death response. Nucleic Acids Res 34:2173–2185. doi:10.1093/nar/gkl238 PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Vasilyeva A, Clodfelter JE, Gorczynski MJ, et al. (2010) Parameters of reserpine analogs that induce MSH2/MSH6-dependent cytotoxic response. J Nucleic Acids, Article ID 162018, doi: 10.4061/2010/162018
  93. 93.
    Lange O, Lakomek N, Farès C (2008) Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution. Science 320:1471–1475PubMedCrossRefGoogle Scholar
  94. 94.
    Stojic L, Brun R, Jiricny J (2004) Mismatch repair and DNA damage signalling. DNA Repair (Amst) 3:1091–1101. doi:10.1016/j.dnarep.2004.06.006 CrossRefGoogle Scholar
  95. 95.
    Fishel R, Wilson T (1997) MutS homologs in mammalian cells. Curr Opin Genet Dev 7:105–113PubMedCrossRefGoogle Scholar
  96. 96.
    Kolodner RD, Marsischky GT (1999) Eukaryotic DNA mismatch repair. Curr Opin Genet Dev 9:89–96. doi:10.1016/S0959-437X(99)80013-6 PubMedCrossRefGoogle Scholar
  97. 97.
    Bellacosa A (2001) Functional interactions and signaling properties of mammalian DNA mismatch repair proteins. Cell Death Differ 8:1076–1092. doi:10.1038/sj.cdd.4400948 PubMedCrossRefGoogle Scholar
  98. 98.
    Kunkel TA, Erie DA (2005) DNA mismatch repair. Annu Rev Biochem 74:681–710. doi:10.1146/annurev.biochem.74.082803.133243 PubMedCrossRefGoogle Scholar
  99. 99.
    Drotschmann K, Topping RP, Clodfelter JE, Salsbury FR (2004) Mutations in the nucleotide-binding domain of MutS homologs uncouple cell death from cell survival. DNA Repair (Amst) 3:729–742. doi:10.1016/j.dnarep.2004.02.011 CrossRefGoogle Scholar
  100. 100.
    Abdelhafez OM, Amin KM, Ali HI, Abdalla M, Ahmed EY (2014) RSC Adv 4:11569–11579. doi:10.1039/c4ra00943f CrossRefGoogle Scholar
  101. 101.
    Negureanu L, Salsbury FR (2012) The molecular origin of the MMR-dependent apoptosis pathway from dynamics analysis of MutSα-DNA complexes. J Biomol Struct Dyn 30:1–15. doi:10.1080/07391102.2012.680034 CrossRefGoogle Scholar
  102. 102.
    Negureanu L, Salsbury FR (2014) Non-specificity and synergy at the binding site of the carboplatin-induced DNA adduct via molecular dynamics simulations of the MutSα-DNA recognition complex. J Biomol Struct Dyn 32:969–992. doi:10.1080/07391102.2013.799437 PubMedCrossRefGoogle Scholar
  103. 103.
    Baiz D, Pinder T, Hassan S (2012) Synthesis and characterization of a novel prostate cancer-targeted phosphatidylinositol-3-kinase inhibitor prodrug. J Med Chem 55:8038–8046. doi:10.1021/jm300881a PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Cohen MB, Rokhlin OW (2009) Mechanisms of prostate cancer cell survival after inhibition of AR expression. J Cell Biochem 106:363–371. doi:10.1002/jcb.22022 PubMedCrossRefGoogle Scholar
  105. 105.
    Woods CJ, Malaisree M, Pattarapongdilok N et al (2012) Long time scale GPU dynamics reveal the mechanism of drug resistance of the dual mutant I223R/H275Y neuraminidase from H1N1-2009 influenza virus. Biochemistry 51:4364PubMedCrossRefGoogle Scholar
  106. 106.
    Yuan Y, Knaggs MH, Poole LB et al (2010) Conformational and oligomeric effects on the cysteine pK(a) of tryparedoxin peroxidase. J Biomol Struct Dyn 28:51–70. doi:10.1080/07391102.2010.10507343 PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Salsbury FR, Yuan Y, Knaggs MH et al (2012) Structural and electrostatic asymmetry at the active site in typical and atypical peroxiredoxin dimers. J Phys Chem B 116:6832–6843. doi:10.1021/jp212606k PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Rhee SG, Kang SW, Jeong W et al (2005) Intracellular messenger function of hydrogen peroxide and its regulation by peroxiredoxins. Curr Opin Cell Biol 17:183–189. doi:10.1016/j.ceb.2005.02.004 PubMedCrossRefGoogle Scholar
  109. 109.
    Sue GR, Ho ZC, Kim K (2005) Peroxiredoxins: a historical overview and speculative preview of novel mechanisms and emerging concepts in cell signaling. Free Radic Biol Med 38:1543–1552. doi:10.1016/j.freeradbiomed.2005.02.026 CrossRefGoogle Scholar
  110. 110.
    Wood ZA, Schroder E, Robin Harris J, Poole LB (2003) Structure, mechanism and regulation of peroxiredoxins. Trends Biochem Sci 28:32–40. doi:10.1016/S0968-0004(02)00003-8 PubMedCrossRefGoogle Scholar
  111. 111.
    Kube S, Weber M (2007) A coarse graining method for the identification of transition rates between molecular conformations. J Chem Phys 126:024103. doi:10.1063/1.2404953 PubMedCrossRefGoogle Scholar
  112. 112.
    Bernini A, Henrici De Angelis L, Morandi E et al (2014) Searching for protein binding sites from Molecular Dynamics simulations and paramagnetic fragment-based NMR studies. Biochim Biophys Acta 1844:561–566. doi:10.1016/j.bbapap.2013.12.012 PubMedCrossRefGoogle Scholar
  113. 113.
    Budiman ME, Knaggs MH, Fetrow JS, Alexander RW (2007) Using molecular dynamics to map interaction networks in an aminoacyl-tRNA synthetase. Proteins 68:670–689. doi:10.1002/prot.21426 PubMedCrossRefGoogle Scholar
  114. 114.
    Kalyaanamoorthy S, Chen Y-PP (2014) Modelling and enhanced molecular dynamics to steer structure-based drug discovery. Prog Biophys Mol Biol 114:123–136. doi:10.1016/j.pbiomolbio.2013.06.004 PubMedCrossRefGoogle Scholar
  115. 115.
    Kobilka BK (2007) G protein coupled receptor structure and activation. Biochim Biophys Acta 1768:794–807. doi:10.1016/j.bbamem.2006.10.021 PubMedCrossRefGoogle Scholar
  116. 116.
    Patny A, Desai PV, Avery MA (2006) Homology modeling of G-protein-coupled receptors and implications in drug design. Curr Med Chem 13:1667–1691. doi:10.2174/092986706777442002 PubMedCrossRefGoogle Scholar
  117. 117.
    Bhattacharya S, Lam AR, Li H et al (2013) Critical analysis of the successes and failures of homology models of G protein-coupled receptors. Proteins 81:729–739. doi:10.1002/prot.24195 PubMedPubMedCentralCrossRefGoogle Scholar
  118. 118.
    Borhani DW, Shaw DE (2012) The future of molecular dynamics simulations in drug discovery. J Comput Aided Mol Des 26:15–26. doi:10.1007/s10822-011-9517-y PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ryan C. Godwin
    • 1
  • Ryan Melvin
    • 1
  • Freddie R. SalsburyJr.
    • 1
  1. 1.Department of PhysicsWake Forest UniversityWinston-SalemUSA

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